The subject matter disclosed herein relates to image reconstruction, and in particular to the use of machine learning techniques, such as deep learning, to segment bone, tissue or other types of materials in derived images.
Non-invasive imaging technologies allow images of the internal structures or features of a patient/object to be obtained without performing an invasive procedure on the patient/object. In particular, such non-invasive imaging technologies rely on various physical principles (such as the differential transmission of X-rays through the target volume, the reflection of acoustic waves within the volume, the paramagnetic properties of different tissues and materials within the volume, the breakdown of targeted radionuclides within the body, and so forth) to acquire data and to construct images or otherwise represent the observed internal features of the patient/object.
Certain imaging techniques may be employed for imaging particular structures and/or obtaining particular types of anatomical or physiological information. By way of example, computed tomography (CT) angiography is a technique employed for imaging the vasculature of a patient. It is typically desirable to remove bone from the reconstructed images to allow an unobstructed view of the vascular tree. Such bone removal operations, however, may be difficult due to the contrast agents employed in the vascular imaging process rendering the imaged vessels highly radio opaque, much like the bone structures to be removed from the final images.
In particular, traditional segmentation approaches use adaptive thresholding in a joint histogram domain of the multi-channel data to solve the problem of bone segmentation from CT data. In this process, features like mean, standard deviation, and so forth are needed for the threshold selection. However, overlapping intensity values of tissue and bone, such as due to the use of a contrast agent in the vasculature, may lead to misclassification using this method due to overlapping intensity values.